Fix and document test_datasets

README.md

@@ -607,6 +607,17 @@ datasets:
       # For `completion` datsets only, uses the provided field instead of `text` column
       field:
 
+# A list of one or more datasets to eval the model with.
+# You can use either test_datasets, or val_set_size, but not both.
+test_datasets:
+  - path: /workspace/data/eval.jsonl
+    ds_type: json
+    # You need to specify a split. For "json" datasets the default split is called "train".
+    split: train
+    type: completion
+    data_files:
+      - /workspace/data/eval.jsonl
+
 # use RL training: dpo, ipo, kto_pair
 rl:
 

src/axolotl/core/trainer_builder.py

@@ -735,7 +735,7 @@ def build(self, total_num_steps):
         elif self.cfg.sample_packing and self.cfg.eval_sample_packing is False:
             training_arguments_kwargs["dataloader_drop_last"] = True
 
-        if self.cfg.val_set_size == 0:
+        if not self.cfg.test_datasets and self.cfg.val_set_size == 0:
             # no eval set, so don't eval
             training_arguments_kwargs["evaluation_strategy"] = "no"
         elif self.cfg.eval_steps:
@@ -822,6 +822,7 @@ def build(self, total_num_steps):
                 self.cfg.load_best_model_at_end is not False
                 or self.cfg.early_stopping_patience
             )
+            and not self.cfg.test_datasets
             and self.cfg.val_set_size > 0
             and self.cfg.save_steps
             and self.cfg.eval_steps

src/axolotl/utils/data.py

@@ -440,7 +440,7 @@ def load_prepare_datasets(
     split="train",
 ) -> Tuple[Dataset, Dataset, List[Prompter]]:
     dataset, prompters = load_tokenized_prepared_datasets(
-        tokenizer, cfg, default_dataset_prepared_path
+        tokenizer, cfg, default_dataset_prepared_path, split=split
     )
 
     if cfg.dataset_shard_num and cfg.dataset_shard_idx is not None: